Exploiting Unsupervised Data for Emotion Recognition in Conversations (2020.findings-emnlp)
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| Challenge: | Existing models for Emotion Recognition in Conversations lack supervised data, which prevents them from playing their maximum effect. |
| Approach: | They propose a Conversation Completion task which uses unsupervised conversation data to leverage unsupervised data. |
| Outcome: | The proposed model improves on the minority emotion classes on the ERC datasets. |
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| Challenge: | Existing methods to model context of utterances and speaker are inadequate . despite the improvements, there are still intrinsic challenges in the ERC dataset . |
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| Challenge: | Existing studies on Emotion Recognition in Conversations (ERC) focus on training and testing models on the same datasets and there is no prior work on adaptability. |
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| Challenge: | Emotion Recognition in Conversation (ERC) aims to analyze the speaker’s emotional state in a conversation. |
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| Challenge: | Emotion recognition in conversation (ERC) is an advanced capability of conversational AI systems. |
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| Challenge: | Existing research on emotion recognition in conversation does not reach a consensus on classification theories . despite this, there is no clear consensus on how to recognize previously unseen emotions in real-world applications. |
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| Challenge: | Emotion recognition in conversations (ERC) is a task that aims to recognize the emotion of each utterance in conversations. |
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| Challenge: | Existing methods to capture emotions in conversation (ERC) lack the correlation between emotions and semantics, resulting in many challenges. |
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| Challenge: | Emotion recognition in conversation research suffers from data imbalance and the presence of similar linguistic expressions for different emotions. |
| Approach: | They propose a Contrast-Enhanced Prompt-Tuning framework that transforms an ERC task into a Masked Language Modeling task and generates the emotion for each utterance in the conversation. |
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CoMPM: Context Modeling with Speaker’s Pre-trained Memory Tracking for Emotion Recognition in Conversation (2022.naacl-main)
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| Challenge: | Emotion recognition in conversation is inaccurate if the previous utterances are not taken into account, so many studies reflect the dialogue context to improve the performance. |
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